Browsing repeats in genomes: Pygram and an application to non-coding region analysis

Background  

A large number of studies on genome sequences have revealed the major role played by repeated sequences in the structure, function, dynamics and evolution of genomes. In-depth repeat analysis requires specialized methods, including visualization techniques, to achieve optimum exploratory power.     

Generalized method of moments for estimating parameters of stochastic reaction networks

Background

Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.

Results

We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.

Conclusions

The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.

     

Regularized estimation of large-scale gene association networks using graphical Gaussian models

Background  

Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. Popular approaches include biased estimates of the covariance matrix and high-dimensional regression schemes, such as the Lasso and Partial Least Squares.     

Influence of head models on EEG simulations and inverse source localizations

Background  

The structure of the anatomical surfaces, e.g., CSF and gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the scalp potentials and the inverse source localizations. This was examined in detail with four different human head models.     

A bag-of-words approach for Drosophila gene expression pattern annotation

Background  

Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.     

Influence of head models on neuromagnetic fields and inverse source localizations

Background  

The magnetoencephalograms (MEGs) are mainly due to the source currents. However, there is a significant contribution to MEGs from the volume currents. The structure of the anatomical surfaces, e.g., gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the MEGs and the inverse source localizations. This was examined in detail with three different human head models.     

Development and validation of an oligonucleotide microarray to characterise ectomycorrhizal fungal communities

Background  

In forest ecosystems, communities of ectomycorrhizal fungi (ECM) are influenced by several biotic and abiotic factors. To understand their underlying dynamics, ECM communities have been surveyed with ribosomal DNA-based sequencing methods. However, most identification methods are both time-consuming and limited by the number of samples that can be treated in a realistic time frame. As a result of ongoing implementation, the array technique has gained throughput capacity in terms of the number of samples and the capacity for parallel identification of several species. Thus far, although phylochips (microarrays that are used to detect species) have been mostly developed to trace bacterial communities or groups of specific fungi, no phylochip has been developed to carry oligonucleotides for several ectomycorrhizal species that belong to different genera.     

RuleMonkey: software for stochastic simulation of rule-based models

Background  

The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.     

Experimental evolution with <Emphasis Type="Italic">E. coli</Emphasis> in diverse resource environments. I. Fluctuating environments promote divergence of replicate populations

Background  

Environmental conditions affect the topology of the adaptive landscape and thus the trajectories followed by evolving populations. For example, a heterogeneous environment might lead to a more rugged adaptive landscape, making it more likely that replicate populations would evolve toward distinct adaptive peaks, relative to a uniform environment. To date, the influence of environmental variability on evolutionary dynamics has received relatively little experimental study.     

Computation of the kinematics and the minimum peak joint moments of sit-to-stand movements

Background  

A sit-to-stand (STS) movement requires muscle strength higher than that of other daily activities. There are many elderly people, who experience difficulty when standing up from a chair. The muscle strength required (or the load on the joints) during a STS task is determined by the kinematics (movement pattern). The purpose of this study was to evaluate the kinematics and resultant joint moments of people standing up from a chair in order to determine the minimum peak joint moments required for a STS task.     

Polylepis woodland dynamics during the last 20,000 years

Aim

To determine the palaeoecological influences of climate change and human land use on the spatial distribution patterns of Polylepis woodlands in the Andes.

Location

Tropical Andes above 2,900 m between 2°S and 18°S of latitude.

Methods

Pollen and charcoal data were gathered from 13 Andean lake sediment records and were rescaled by the maximum value in each site. The rescaled pollen data were used to estimate a mean abundance and coefficient of variation to show woodland expansions/contractions and woodland fragmentation over the last 20,000 years. The rescaled charcoal was displayed as a 200‐year moving median using 500‐year bins to infer the influence of fire on woodland dynamics at landscape scale. Pollen and charcoal were compared with speleothem, clastic flux and archaeological data to assess the influence of moisture balance, glacial activity and human impact on the spatial distribution of Polylepis woodlands.

Results

Woodland expansion and fire were correlated with precipitation changes and glacier dynamics from c. 20 to 6 kcal bp (thousands of calibrated years before present). Charcoal abundances between 20 and 12 kcal bp were less common than from 12 kcal bp to modern. However, human‐induced fires were unlikely to be the main cause of a woodland decline centred at 11 kcal bp , as woodlands recovered from 10.5 to 9.5 kcal bp (about twofold increase). Charcoal peaks analogous to those that induced the woodland decline at 11 kcal bp were commonplace post‐9.5 kcal bp but did not trigger an equivalent woodland contraction. An increase in the coefficient of variation after c. 5.5 kcal bp suggests enhanced fragmentation and coincided with the shift from logistic to exponential growth of human populations. Over the last 1,000 years, Polylepis became hyper‐fragmented with over half of sites losing Polylepis from the record and with coefficients of variation paralleling those of glacial times.

Main conclusions

Polylepis woodlands formed naturally patchy woodlands, rather than a continuous vegetation belt, prior to human occupation in the Andes. The main factors controlling pre‐human woodland dynamics were precipitation and landscape heterogeneity. Human activity led to hyper‐fragmentation during the last c. 1,000 years.     

Extracting key information from historical data to quantify the transmission dynamics of smallpox

Background  

Quantification of the transmission dynamics of smallpox is crucial for optimizing intervention strategies in the event of a bioterrorist attack. This article reviews basic methods and findings in mathematical and statistical studies of smallpox which estimate key transmission parameters from historical data.     

Complexity of MRI induced heating on metallic leads: Experimental measurements of 374 configurations

Background  

MRI induced heating on PM leads is a very complex issue. The widely varying results described in literature suggest that there are many factors that influence the degree of heating and that not always are adequately addressed by existing testing methods.     

Stochastic synchronization of genetic oscillator networks

Background  

The study of synchronization among genetic oscillators is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. Genetic networks are intrinsically noisy due to natural random intra- and inter-cellular fluctuations. Therefore, it is important to study the effects of noise perturbation on the synchronous dynamics of genetic oscillators. From the synthetic biology viewpoint, it is also important to implement biological systems that minimizing the negative influence of the perturbations.     

Flow cytometric monitoring of influenza A virus infection in MDCK cells during vaccine production

Background  

In cell culture-based influenza vaccine production the monitoring of virus titres and cell physiology during infection is of great importance for process characterisation and optimisation. While conventional virus quantification methods give only virus titres in the culture broth, data obtained by fluorescence labelling of intracellular virus proteins provide additional information on infection dynamics. Flow cytometry represents a valuable tool to investigate the influences of cultivation conditions and process variations on virus replication and virus yields.     

High-throughput identification of interacting protein-protein binding sites

Background  

With the advent of increasing sequence and structural data, a number of methods have been proposed to locate putative protein binding sites from protein surfaces. Therefore, methods that are able to identify whether these binding sites interact are needed.     

A Many-Body Field Theory Approach to Stochastic Models in Population Biology

Background

Many models used in theoretical ecology, or mathematical epidemiology are stochastic, and may also be spatially-explicit. Techniques from quantum field theory have been used before in reaction-diffusion systems, principally to investigate their critical behavior. Here we argue that they make many calculations easier and are a possible starting point for new approximations.

Methodology

We review the many-body field formalism for Markov processes and illustrate how to apply it to a ‘Brownian bug’ population model, and to an epidemic model. We show how the master equation and the moment hierarchy can both be written in particularly compact forms. The introduction of functional methods allows the systematic computation of the effective action, which gives the dynamics of mean quantities. We obtain the 1-loop approximation to the effective action for general (space-) translation invariant systems, and thus approximations to the non-equilibrium dynamics of the mean fields.

Conclusions

The master equations for spatial stochastic systems normally take a neater form in the many-body field formalism. One can write down the dynamics for generating functional of physically-relevant moments, equivalent to the whole moment hierarchy. The 1-loop dynamics of the mean fields are the same as those of a particular moment-closure.     

Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data

Background  

With the advance of microarray technology, several methods for gene classification and prognosis have been already designed. However, under various denominations, some of these methods have similar approaches. This study evaluates the influence of gene expression variance structure on the performance of methods that describe the relationship between gene expression levels and a given phenotype through projection of data onto discriminant axes.     

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images

Background  

A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods.